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DOC: clarify "inplace"-ness of DataFrame.setitem #51328

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Merged
merged 7 commits into from
Feb 20, 2023
Merged

DOC: clarify "inplace"-ness of DataFrame.setitem #51328

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3a597f4
DOC: clarify inplace-ness of DataFrame.setitem
loicdiridollou Feb 11, 2023
ec260fc
Update pandas/core/frame.py
loicdiridollou Feb 13, 2023
bfda14c
Update pandas/core/frame.py
loicdiridollou Feb 13, 2023
87b454d
Update pandas/core/frame.py
loicdiridollou Feb 13, 2023
f267346
Merge branch 'pandas-dev:main' into master
loicdiridollou Feb 13, 2023
85b9a77
Updating docstring of pandas/core/frame.py::isetitem with feedback.
loicdiridollou Feb 13, 2023
8805f78
Update pandas/core/frame.py
jorisvandenbossche Feb 17, 2023
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17 changes: 10 additions & 7 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3867,23 +3867,26 @@ def _get_value(self, index, col, takeable: bool = False) -> Scalar:

def isetitem(self, loc, value) -> None:
"""
Set the given value in the column with position 'loc'.
Set the given value in the column with position `loc`.

This is a positional analogue to __setitem__.
This is a positional analogue to `__setitem__`.

Parameters
----------
loc : int or sequence of ints
Index position for the column.
value : scalar or arraylike
Value(s) for the column.

Notes
-----
Unlike `frame.iloc[:, i] = value`, `frame.isetitem(loc, value)` will
_never_ try to set the values in place, but will always insert a new
array.
``frame.isetimtem(loc, value)`` is an in-place method as it will
modify the frame in place (not returning a new object) but it will
not update the values of the column itself, it will instead insert
a new array.
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I would add something like "in contrast to frame.iloc[:, i] = value which will try to update the existing values inplace"

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I just made some examples on my side and indeed this is quite important to keep that in the docs, I will update that

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We should be good now, let me know if the phrasing is clear enough

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We should be good now, let me know if the phrasing is clear enough


In cases where `frame.columns` is unique, this is equivalent to
`frame[frame.columns[i]] = value`.
In cases where ``frame.columns`` is unique, this is equivalent to
``frame[frame.columns[i]] = value``.
"""
if isinstance(value, DataFrame):
if is_scalar(loc):
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